#encoding=utf8
import numpy as np
import pickle
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
import os
import sys
from tqdm import tqdm
import matplotlib.pyplot as plt
import seaborn as sns

# 设置Python的递归深度以避免可能的Stack Overflow错误
sys.setrecursionlimit(2000)

def load_dataset(file_name):
    '''
    从文件读入数据集
    被多处调用，请勿删除或改动本函数！！！
    '''
    try:
        with open(file_name, 'rb') as f:
            raw_dataset = pickle.load(f)
    except FileNotFoundError:
        print(f"错误: 文件 {file_name} 未找到。请确保文件路径正确。")
        return None, None
        
    try:
        example_image = raw_dataset[0][0]
    except KeyError:
        print("错误: 数据集格式不正确，无法找到类别0的数据。")
        return None, None
    except TypeError:
        print("错误: 数据集格式不正确，类别0的数据不是列表或数组。")
        return None, None

    dataset = np.empty((0, example_image.size))
    labels = np.empty((0, 1))

    for i_class in raw_dataset.keys():
        images_list = raw_dataset.get(i_class, [])
        if not isinstance(images_list, list) or len(images_list) == 0:
            print(f"警告：类别 {i_class} 的数据格式不正确或为空，已跳过。")
            continue
            
        for image in images_list:
            features = image.flatten()
            dataset = np.vstack((dataset, features))
            labels = np.vstack((labels, i_class))

    return dataset, labels

class Classifier:
    def __init__(self):
        self.model = None
        self.train_dataset, self.train_labels = load_dataset('./step1/input/training_dataset.pkl')
        if self.train_dataset is None:
            raise RuntimeError("训练数据集加载失败，无法继续。")

    def train(self):
        #********* BEGIN *********#
        print("\n=== 正在寻找最佳超参数 (C 和 gamma) ... ===")
        
        # 扩展超参数网格，以进行更全面的搜索
        param_grid = {
            'C': [0.1, 1, 10, 100, 500, 1000],
            'gamma': [0.0001, 0.001, 0.01, 0.1, 'scale']
        }
        
        svm_model = SVC(kernel='rbf', random_state=42)
        
        grid_search = GridSearchCV(
            svm_model, 
            param_grid, 
            cv=5, 
            n_jobs=-1, 
            verbose=2,
            scoring='accuracy'
        )
        
        # 训练过程，GridSearchCV会自动进行交叉验证
        grid_search.fit(self.train_dataset, self.train_labels.ravel())

        print("\n=== 超参数搜索完成 ===")
        print("最佳参数: ", grid_search.best_params_)
        print("最佳交叉验证正确率: {:.4f}".format(grid_search.best_score_))
        
        # 使用找到的最佳参数重新训练模型
        self.model = grid_search.best_estimator_
        print("=== 最终模型训练完成 ===")

        # 生成并保存热力图
        self.generate_heatmap(grid_search, param_grid)

        #********* END *********#

    def generate_heatmap(self, grid_search, param_grid):
        print("\n=== 正在生成超参数准确率热力图... ===")
        
        # 提取网格搜索结果
        results = grid_search.cv_results_
        scores = results['mean_test_score']
        
        # 将结果重塑为二维矩阵，用于绘制热力图
        scores_matrix = np.array(scores).reshape(len(param_grid['C']), len(param_grid['gamma']))
        
        # 绘制热力图
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(10, 8))
        
        # 替换gamma标签中的'scale'为更友好的中文
        gamma_labels = [f'{g}' for g in param_grid['gamma']]
        if 'scale' in gamma_labels:
            gamma_labels = ['自适应' if x == 'scale' else x for x in gamma_labels]

        ax = sns.heatmap(
            scores_matrix,
            annot=True,
            fmt=".4f",
            cmap="RdYlGn",
            xticklabels=gamma_labels,
            yticklabels=param_grid['C'],
            vmin=scores.min(),
            vmax=scores.max()
        )
        
        ax.set_title('SVM 超参数准确率热力图', fontsize=16)
        ax.set_xlabel('gamma (核函数系数)', fontsize=12)
        ax.set_ylabel('C (惩罚系数)', fontsize=12)
        plt.tight_layout()
        
        # 确保输出目录存在
        output_dir = './step1/output/svm_grid_search'
        os.makedirs(output_dir, exist_ok=True)
        figure_path = os.path.join(output_dir, 'svm_heatmap.png')
        
        plt.savefig(figure_path)
        plt.close()
        print(f"超参数热力图已保存为 '{figure_path}'")

    def predict(self, test_dataset):
        '''
        输入：测试数据 test_dataset: 形状为(500, 784)的ndarray
        输出：预测结果 predicted_labels: 形状为(500, )的ndarray
        '''
        #********* BEGIN *********#
        predicted_labels = self.model.predict(test_dataset)
        #********* END *********#
        return predicted_labels

def calculate_accuracy(file_name, classifier):
    test_dataset, test_labels = load_dataset(file_name)
    if test_dataset is None:
        return 0
        
    random_indices = np.random.permutation(test_dataset.shape[0])
    test_dataset = test_dataset[random_indices,:]
    test_labels = test_labels[random_indices,:]
    predicted_labels = classifier.predict(test_dataset)
    if isinstance(predicted_labels, np.ndarray):
        if predicted_labels.size != test_labels.size:
            print('错误：输出的标签数量与测试集大小不一致')
            accuracy = 0
        else:
            accuracy = np.mean(predicted_labels.flatten()==test_labels.flatten())
    else:
        print('错误：输出格式有误，必须为ndarray格式')
        accuracy = 0
    return accuracy

if __name__ == '__main__':
    classifier = Classifier()
    classifier.train()

    sum_accuracies = 0
    num_test_datasets = 0

    test_dir = './step1/input'
    
    test_files = ['test_dataset_clean.pkl'] + [
        f'test_dataset_noise_type{noise}_level{level}.pkl'
        for noise in range(1, 7)
        for level in range(1, 4)
    ]

    print("\n=== 正在对所有测试集进行评估... ===")
    with tqdm(total=len(test_files), desc="正在测试", file=sys.stdout) as pbar:
        for file_name in test_files:
            file_path = os.path.join(test_dir, file_name)
            
            pbar.set_description(f"正在测试: {file_name}")
            accuracy = calculate_accuracy(file_path, classifier)
            pbar.set_postfix({'正确率': f'{accuracy:.4f}'})
            pbar.update(1)

            sum_accuracies += accuracy
            num_test_datasets += 1
    
    mean_accuracies = sum_accuracies / num_test_datasets
    print(f'\n你在总共{num_test_datasets}个测试集上的平均正确率为：{mean_accuracies:.4f}')